pyriemann.transfer.TLClassifier¶
- class pyriemann.transfer.TLClassifier(target_domain, estimator, domain_weight=None)¶
Transfer learning wrapper for classifiers.
This is a wrapper for any classifier that converts extended labels used in Transfer Learning into the usual y array to train a classifier of choice.
- Parameters
- target_domainstr
Domain to consider as target.
- estimatorBaseClassifier
The classifier to apply on matrices.
- domain_weightNone | dict, default=None
Weights to combine matrices from each domain to train the classifier. The dict contains key=domain_name and value=weight_to_assign. If None, it uses equal weights.
Notes
New in version 0.4.
- __init__(target_domain, estimator, domain_weight=None)¶
Init.
- fit(X, y_enc)¶
Fit TLClassifier.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- y_encndarray, shape (n_matrices,)
Extended labels for each matrix.
- Returns
- selfTLClassifier instance
The TLClassifier instance.
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- predict(X)¶
Get the predictions.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- predndarray, shape (n_matrices,)
Predictions for each matrix according to the estimator.
- predict_proba(X)¶
Get the probability.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Set of SPD matrices.
- Returns
- predndarray, shape (n_matrices, n_classes)
Predictions for each matrix.
- score(X, y_enc)¶
Return the mean accuracy on the given test data and labels.
- Parameters
- Xndarray, shape (n_matrices, n_channels, n_channels)
Test set of SPD matrices.
- y_encndarray, shape (n_matrices,)
Extended true labels for each matrix.
- Returns
- scorefloat
Mean accuracy of self.predict(X) wrt. y.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.